A Suggested Design of an Information System to Enhance the Efficiency of Intermediate Work Forces by the Use of Neural Network Technology - A Case Study at the University of Kufa

	A Suggested Design of an Information System to Enhance the Efficiency of Intermediate Work Forces by the Use of Neural Network Technology - A Case Study at the University of Kufa
AL GHAREE for Economics and Administration Sciences
Article 1, Volume 9, Issue 27, 2013, Pages 129-156
Abstract
The intermediate work forces play a key role in the implementation of the plans and strategies adopted by the University of Kufa and works to achieve them. Although the university's keenness to maintain the competencies of this staff, but the staff and officials face the problem of staff when change their positions or transfer to other functions. Such new positions and places may not correspond perfectly with the types of previous experience and skills acquired resulting in the loss of those capabilities and disperse. Perhaps one of the most prominent observable phenomena that led to the emergence of the problem is the magnitude of the current system work and the many branches. The Blurred indicators adopted to conduct the transfer process and functional change locations. This research tries to identify key indicators to measure the competencies and capabilities of this type of administrative work power. No specific variables were found to suit all institutions due to different fields and working conditions. Also no previous studies were carried out to focus on this aspect in academic institutions. Hence this research in an attempt to design an information system using neural networks technology. The Neural Network Technology as one of the artificial intelligence techniques that can measure proposed ten indicators. This research is to highlight their role in decision-making relating to the transfer such employees or change their career. This investigation reached a series of important results, notably the presence of many indicators that can be used in decision to move or transfer staff. It is clearly found that the ability of neural networks to contribute to solving this problem by feeding those indicators. Therefore the most important recommendations of the research were focused on the need to adopt such indicators in the decision of transport and functional change locations. In conclusion it was found that using neural network technology as one of the smart information systems to maintain the expertise and competencies of staff, not only at the University of Kufa, but in other universities as a result of the flexibility of this program.

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